| name | aviation-fdm-foqa-analysis |
| description | Structure a flight-data-monitoring (FDM/FOQA) INFORMED safety analysis from the exceedance and event summaries the user supplies — frame the findings, the trends, and the SMS actions per the ICAO Annex 19 Safety Assurance pillar. It does NOT ingest or analyse raw flight data and does NOT invent exceedance counts or values; absent data is recorded as [GAP] and routed to the competent FDM team. Use this skill to structure an FDM/FOQA-informed analysis for a named operator from summaries the user provides. Grounds in KB-STD-ICAO-ANNEX19, traces every finding to a supplied summary item, reaches systemic SMS actions, and is explicitly assistive. Decision-support only; a competent person must review the output. |
| license | Apache-2.0 |
| metadata | {"author":"eyekyam","version":"1.0","category":"performance","tier":4,"audience":["M","E","C"],"industry":["Avi"],"jurisdiction":["All"],"status":"assistive","plugin":"hse-aviation","hse_reviewed_by":"","hse_reviewed_date":""} |
Aviation FDM / FOQA Analysis (assistive)
The FDM/FOQA-informed analysis skill (ICAO Annex 19 Pillar 3 — Safety Assurance; status: assistive). For a named operator it structures a safety analysis from the exceedance and event summaries the user supplies — framing the findings, the trends, and the SMS actions, each traced to a supplied summary item, reaching systemic SMS findings (the B5 evidence-traced discipline). It does NOT ingest or analyse raw flight data, and it does NOT invent exceedance counts or values — an absent datum is recorded as [GAP] and routed to the competent FDM team. This honest assistive boundary is the point: an LLM skill cannot analyse raw FDM/FOQA parameter data, so it structures the output of that analysis, never the raw data itself.
When to use this skill
Use this skill when the user has FDM/FOQA exceedance / event summaries already and needs them structured into findings, trends, and SMS actions for a named operator. Trigger phrases: "structure our FOQA exceedance summary into SMS findings", "frame these flight-data-monitoring events", "turn our FDM summary into actions". Do NOT use it expecting raw-data analytics — it analyses neither raw parameter data nor invents exceedance values. If the request is vague, the Workflow intake forces the named operator and the supplied summaries first.
Data Protection & De-identification (MANDATORY — apply before drafting)
Apply this BEFORE you draft anything. Treat injury, illness, and any health
detail as the highest sensitivity. Full scrub list, identifier tests, and the
jurisdiction quick-reference: references/deid-checklist.md.
- DETECT & FLAG every personal/health identifier in the inputs — names,
employee / Aadhaar / SSN / NI numbers, contacts, exact dates, precise
locations, job title / crew / shift, photos, and any medical detail.
List what you found before drafting. If unsure whether something is
identifying, treat it as identifying.
- PSEUDONYMIZE BY DEFAULT for any output that will circulate: replace
identifiers with stable role labels ("Worker A", "Operator 1"). Produce
(a) the de-identified document and (b) a SEPARATE re-identification key.
Never put the key or any name↔label mapping in the document. Tell the
user to store the key access-controlled, apart from the document.
- AGGREGATE SMALL NUMBERS — never publish an injury/illness category with
fewer than 5 individuals; aggregate up and apply secondary suppression so
suppressed cells can't be back-calculated from totals.
- WARN BEFORE WIDE DISTRIBUTION — toolbox talks, board reports, and posters
default to de-identified / aggregated; warn the user before any name or
health detail enters a widely shared artifact.
- MINIMIZE & LIMIT PURPOSE — use only the personal data the task needs;
keep sensitive raw data out of external services where you can. When in
doubt, ask before including it.
Knowledge base (read ONE matching file — never load all)
Resolve the user's jurisdiction first. Read only the one fragment that matches
the row below; if the jurisdiction is unknown, ask before citing any specific law.
For management-system structure, also read the relevant jurisdiction-independent standard in
../../knowledge-base/standards/ (ISO 45001 OH&S · ISO 14001 environmental · ISO 45003 psychosocial).
Always apply ../../knowledge-base/prompt-snippets/hierarchy-of-controls.md (KB-SNIP-HOC)
to every control recommendation. For any benchmark/figure, look up the ID in the relevant
_registry.yaml, then read ONLY the named file — and quote its source+year.
| Jurisdiction | Read |
|---|
| Any (SMS Pillar 3) | ../../knowledge-base/standards/icao-annex19.md (KB-STD-ICAO-ANNEX19 — Pillar 3 Safety Assurance / operational-data monitoring) + prompt-snippets/hierarchy-of-controls.md (KB-SNIP-HOC) |
| Unknown | Ask the operator's certificating authority before aligning to a State programme |
Workflow
Open with a structured multi-step intake — MCQ where the answer space is enumerable, free-text where it is open. Ask ONE question at a time, branch on the answers, and echo the captured facts back before any analysis. Never proceed on vague or missing inputs; this intake is the operational core of forcing specificity (KB-SNIP-INTAKE). (Intake is a Workflow convention, not a sixth block.)
Assistive boundary (D-05a) — state it up front: this skill structures analysis from the summaries the user supplies. It does NOT ingest raw flight data and does NOT invent exceedance counts/values; an absent datum is [GAP], routed to the competent FDM team.
The full typed/branched intake Q-table — the named operator/fleet, the CAA/SSP jurisdiction
branch (India → KB-REG-IN-DGCA, FAA/EASA → ask-the-reference, never fabricate), the audience,
the supplied exceedance summaries (the skill works ONLY from these — never raw flight data,
never an invented count/value), the one-off-vs-trend branch (a trend needs ≥2 supplied periods),
the period, and the SMS-action owner — lives in references/intake.md (the intake-coverage
manifest + de-id-aware echo-back, role labels only + refuse-on-vague anchors). Crew detail is
de-identified to role labels FIRST. Run it one question at a time, branch on the answers, and
echo the confirmed operator + supplied summaries back (crew detail de-identified) before any
analysis. Then: frame each finding traced to a supplied summary item; identify trends ONLY across the supplied data; reach systemic SMS findings and HoC-ranked actions with named owners/dates. For any datum the user did NOT supply, record [GAP] and route it to the competent FDM team — never fabricate an exceedance count or value.
Then: validate the draft against references/QUALITY_CHECKLIST.md → produce the output via the Output format section below. The domain method (the FDM/FOQA-informed analysis frame) is in references/METHODOLOGY.md.
Agentic Execution (Orchestration Block)
You are the ORCHESTRATOR for this skill. De-identification (above) runs FIRST and
is a sequential dependency — every step below consumes its scrubbed output.
Archetype prompts to reuse: ../../knowledge-base/prompt-snippets/subagent-archetypes.md (KB-SNIP-ARCHETYPES).
Step 0 — Triage: fan out at all?
Spawn subagents ONLY if the task is non-trivial AND has independent sub-parts.
Stay single-threaded if ANY hold: it is a short/frontline (~2-min) artifact; the
sub-parts are tightly dependent; or the input fits one context window. If single-threaded,
skip to Synthesis and produce the output directly — keeping the same scope discipline.
Step 1 — Plan
Decompose into INDEPENDENT jobs. Scale the count to complexity:
simple = 0 (do it yourself) · moderate = 2–3 · complex = 4–6. Never exceed MAX=6.
Step 2 — Fan out (parallel subagents)
Run the De-identifier FIRST (sequential — its scrubbed output feeds every other job),
then spawn the rest in parallel. Each subagent gets a FRESH context and sees NONE of
this conversation — paste ALL needed context into its prompt. Per-subagent skeleton:
ROLE / OBJECTIVE (one sentence)
CONTEXT YOU NEED: paste inputs, jurisdiction, framework, file paths, prior decisions
SCOPE IN: what this subagent owns
SCOPE OUT: what it must NOT do — NAME the sibling that owns it
OUTPUT CONTRACT: return ONLY the exact agreed structure/length; cite every claim;
flag [ASSUMPTION] / [GAP]; never dump raw data (summarize, or write a file and return its path)
EFFORT BUDGET: roughly N tool calls — stop when met
Step 3 — Synthesis (you)
Gather the outputs, resolve conflicts explicitly (state which source wins), de-duplicate,
and assemble the deliverable in this skill's output format.
Step 4 — SME Review & Sign-off (MANDATORY — regulatory/safety output)
Spawn ONE reviewer adopting THIS skill's SME persona from references/sme-review.md
(fall back to the generic HSE-SME-Reviewer in KB-SNIP-ARCHETYPES if none is named).
Give it the draft + the inputs + the output contract. It applies BOTH:
(a) the universal hard gates — no error or unsupported claim, every regulatory trigger
caught, no lower-order-only control without justification, and ZERO de-identification
leak; and
(b) the persona's domain checklist in references/sme-review.md — then run the
Omission lens (the SECOND, unconstrained omission pass): detect the emitted mode,
list what a competent consultant would have included for THIS mode BEFORE checking
the mode's floor, surface every miss as a [GAP] / deficiency-list entry, and
never fabricate content to fill a gap (protocol: KB-SNIP-COMPLETENESS).
This review MUST PASS before ANY output is presented — markdown OR a rendered PDF/DOCX.
Fix everything it raises and re-run until clean. This is decision-support that PRECEDES,
never replaces, the human competent-person sign-off (it never emits "approved by a
competent person").
Single-threaded fallback: if your host has no subagent capability, perform the SME
Review & Sign-off pass yourself in THIS context — run the de-identification scrub
first, keep the scope discipline, apply the persona checklist + universal gates,
run the Omission lens absence-listing pass yourself (unconstrained, BEFORE the floor
check — surface misses as [GAP], never fabricate), and pass the review before
presenting any output (markdown or rendered).
Subagent roster for THIS skill
Moderate fan-out (the De-identifier runs FIRST as a sequential dependency):
- De-identifier — runs FIRST; scrub any crew named in the supplied summaries into role labels
before any analysis.
- Researcher — from the SUPPLIED summaries only, assemble the exceedance/event items and their
periods; record
[GAP] for any datum the user did not supply. SCOPE-OUT: does NOT generate or
infer exceedance values, and does not draft.
- Drafter — frame each finding traced to a supplied summary item, identify trends across the
supplied data only, and reach systemic SMS actions (HoC-ranked, named owners/dates). SCOPE-OUT:
does not invent exceedance counts/values.
- Critic/QA (MANDATORY) — the Aviation-SMS persona (
KB-SNIP-ARCHETYPES): the load-bearing
assistive check — does this read as structured analysis of user-supplied summaries, NOT autonomous
analysis of raw flight data? Any invented exceedance count/value is a FLAG; every [GAP] is honest.
And ZERO crew-identity leak. Runs the per-skill SME sign-off checklist in
references/sme-review.md (FDM analyst + line-pilot lenses; decision-support; precedes —
never replaces — the human competent-person / FDM-team review).
Simple single-subject tasks run single-threaded — no subagents.
## Output format
Assemble a report.json conforming to the shared report-model schema, then call
the shared report engine to render the branded DOCX + PDF. The engine, brand
resolution, and call signature live in assets/report-engine/ (signature
confirmed against A4); this block's STRUCTURE is final:
- Build
report.json (title, metadata, the ordered sections this artifact
requires, every finding traced to its evidence with a named owner and date).
- Resolve branding: the user's
brand.yaml overrides the Eyekyam default.
- Render both DOCX and PDF from the one
report.json via the shared engine.
- Surface the output paths and a one-line provenance note to the user.
Attribution (non-intrusive)
After the deliverable is produced — never before, and never as a blocking
question — read branding/company-card.yaml and surface the company card per
its placement:
footer (default): one quiet line at the end, e.g.
"Built by Eyekyam · HSE Leadership, operationalised · eyekyam.com".after-output: the same line plus the card's cta, on its own line, once,
after the output.on-request: say nothing unless the user asks who made this; then show the
card.
If show: false, omit attribution entirely — no line, no footer. Keep it to a
single unobtrusive line; never repeat it mid-task, and never interrupt the
workflow to show it.
Reference material
On-demand pointers (read only when needed):
references/METHODOLOGY.md — the domain method this skill applies.references/deid-checklist.md — the full de-identification checklist (A5).references/QUALITY_CHECKLIST.md — the pre-output validation gate.references/_skill-kb.md — the knowledge-base fragments this skill resolves.
